Our tiny world has become a digital world and we'll have Forty times more bytes in the universe by 2020 than there are stars. In the last 2 years alone, more than 90% of the data is floating and sitting in all possible systems and devices in the world currently were simply created. Such gigantic amounts of data – now called Big Data - can mean a lot for companies and can give greater trends and insights about their customers and user behavior. It is hard to process the large volume of data in both unstructured and structured formats using conventional database tools and modeling. Scientific algorithms, methods, and techniques are therefore required to evaluate and analyze Big Data and the requirement for Data Analytics and Data Science classes and courses.
What is Data Science?
Data Science means innovation. Data Science aims to gain trends and insights by examining various data sets that give companies a competitive edge. Data science is a mixture of software, statistics, and mathematics with an integrated business environment domain experience. The Business Intelligence (BI) is another buzz word often misinterpreted with data science. Business Intelligence is mainly concerned with data reporting and analysis but doesn't involve predictive modeling, thereby allowing Business Intelligence to be considered a Data Science subset. Some of the most essential things of data science are the development of predictive models. Data analytics, predictive analytics, business analytics, and data mining, and are other methods of data science. Data Science often deals with Data Visualization and provides users with information in a comprehensible format.
The Need for Data Science
Organizations must use the data to manage their company and expand it. Data science's fundamental aim is to help businesses make better and quick decisions, which will allow them to achieve stronger industry leadership and market share. It will enable them to take tactical strategies in challenging conditions to be successful and to survive. Companies of all scales transition to a data-driven strategy, with sophisticated data analytics as to the focal point for transformation.
Let's see a few examples of why data science is being used by organizations:
- Netflix examines trends to understand what motivates consumer engagement and uses it to make decisions about the next sequence of output.
- Target: On the other side, define the main consumer categories and the specific customer shopping behaviors within such categories. That allows them to direct different audiences on the market.
- P&G uses time series methods to perceive potential demand more accurately, thereby helping them plan production rates more efficiently.
In-Demand Jobs of Data Science; Skills and Salary
Data Scientist is in great demand. The requirement for Data scientists is on the maximum and they are amongst the best paying individuals in the US itself with a mean base salary of $130K. McKinsey's projections indicate that there will be a 50% difference in supply vs. demand for data science experts in the coming years. You may also learn data science and make useful meaning out of Data.
There are plenty of data science jobs that can be difficult to comprehend. The expertise needed for each job position is different. Mathematics is used in the field of data science. Disciplines of computer science and statistics, and also a growing collection of resources that includes Python, R, SQL, Tableau, etc. Today, the Data science experts are in tremendous demand and we will explore the various job roles available in Data Science:
- Data Scientist – A Data Scientist is undoubtedly one of the best job roles and the most sought-after jobs you can find in the world currently. They are responsible for managing raw data, evaluating it using different methods as mentioned above, and providing information in a way that is useful in forecasting market issues. A data scientist makes use of machine learning and forecasts the future based on historical trends as well. The data scientist can earn an average salary of $119,000 per annum.
- Data Analyst – Data analyst is the one analyzing the results. But this technique involves the development of systems that support business users to draw conclusions and improve the quality of the data. Its function is to obtain, analyze, and conduct statistical data analyses. Data Analyst extracts useful information from the data available, using R or SAS. Not only Information Technology companies, but all sorts of businesses in the sector, i.e. automobile, healthcare, insurance, retail, and finance need Data Analysts to operate their business. Data analysts can earn an average salary of $62,000 per year.
- Data Architect – The position of Data Architect is rapidly growing in significance with the growth of big data. Its function is to build data management frameworks for integrating, protecting, and preserving data sources and information from the organization. He is essential for data design, database architecture, data optimization, and data creation. He depends heavily on MS Excel to do mappings of attributes and template tables. The data architects need to master technologies such as Spark, Pig, XML, Hive, and SQL. Data Architect can earn an average salary of $100,000 per annum.
- Data Engineer – They aren't the ones who evaluate data but create a similar software framework to do the work for other professionals. They are qualified to do this because they have a detailed understanding and knowledge of Hadoop and Big Data technologies such as Hive, MapReduce, NoSQL, Pig, SQL technologies. The function is to create, test, and manage processing systems of massive scale. More than 50% of the job is in Data wrangling, where data technicians excel who have software engineering backgrounds. Data engineers can earn an average salary of $95,000 per annum.
- Statistician – A statistician's job is to identify the changing patterns in the market which affect a business's development. The job is to collect the data and turn it into valuable information. Their expertise gives them the power to manage data of all sorts, be it unstructured or structured. The common techniques and tools that they use include SPSS, SAS, Python, Matlab, SQL, Scala, R, etc. They have a clear statistical history, addressing both inferential and descriptive topics. Data Statistician can earn an average salary of $75,000 per annum.